Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services
Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services
复制标题
基于自适应动态规划的云服务模糊神经网络 Web 服务 QoS 预测
DOI：
10.1109/access.2015.2498191
复制
发表时间：
2015-01-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Yi
中科院分区：
文献类型：
--
作者：
Luo, Xiong;Lv, Yixuan;Chen, Yi
近年来，越来越多的传统业务迁移到云计算环境中，在形成跨云服务应用时，服务质量（QoS）成为服务选择和优化服务组合的重要因素。考虑到QoS数据的非线性和动态性，在设计预测精度不理想的QoS预测方法时，很难实现动态预测。因此，探索如何设计一种有效的方法，将一些智能技术融入到QoS预测方法中，以提高预测性能。在本文中，受自适应评论家设计和q -学习技术的启发，我们提出了一种新的QoS预测方法，通过模糊神经网络和自适应动态规划（ADP）的结合，即在线学习方案。该方法从QoS数据中提取模糊规则，并采用ADP方法对模糊规则进行参数学习。并给出了收敛有界性的结果，保证了算法的稳定性。在大规模QoS服务数据集上的实验结果验证了该方法的预测准确性。
Recently, more and more traditional services are being migrated into a cloud computing environment that makes the quality of service (QoS) becomes an important factor for service selection and optimal service composition while forming cross-cloud service applications. Considering the nonlinear and dynamic property of QoS data, it is so difficult to achieve dynamic prediction while designing a QoS prediction method with unsatisfactory prediction accuracy. It is thus desirable to explore how to design an effective approach by incorporating some intelligent techniques into the QoS prediction method to improve prediction performance. In this paper, motivated by the adaptive critic design and Q-learning technique, we propose a novel QoS prediction approach to serve this purpose through the combination of fuzzy neural networks and adaptive dynamic programming (ADP), i.e., an online learning scheme. This approach extracts fuzzy rules from QoS data and employs the ADP method to parameter learning of the fuzzy rules. Moreover, we provide a convergence boundedness result for our proposed approach to guarantee the stability. Experimental results on a large-scale QoS service data set verify the prediction accuracy of our proposed approach.